from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
scikit-learn vs. scikit-learn-intelex (Intel® oneAPI) benchmarks: perfect hyperparameters match¶reporting = Reporting("sklearnex", config="config.yml")
reporting.run()
KNeighborsClassifier_brute_force: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)¶All estimators share the following hyperparameters: algorithm=brute.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_sklearnex | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | NaN | 0.013 | 0.0 | 6.365 | 0.0 | -1 | 100 | NaN | NaN | 0.063 | 0.0 | 0.199 | 0.0 | See | See |
| 3 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | NaN | 0.012 | 0.0 | 6.596 | 0.0 | -1 | 1 | NaN | NaN | 0.048 | 0.0 | 0.251 | 0.0 | See | See |
| 6 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | NaN | 0.012 | 0.0 | 6.706 | 0.0 | 1 | 100 | NaN | NaN | 0.049 | 0.0 | 0.244 | 0.0 | See | See |
| 9 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | NaN | 0.012 | 0.0 | 6.517 | 0.0 | 1 | 1 | NaN | NaN | 0.049 | 0.0 | 0.253 | 0.0 | See | See |
| 12 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | NaN | 0.012 | 0.0 | 6.721 | 0.0 | -1 | 5 | NaN | NaN | 0.049 | 0.0 | 0.242 | 0.0 | See | See |
| 15 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | NaN | 0.012 | 0.0 | 6.726 | 0.0 | 1 | 5 | NaN | NaN | 0.048 | 0.0 | 0.247 | 0.0 | See | See |
| 18 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | NaN | 0.005 | 0.0 | 0.317 | 0.0 | -1 | 100 | NaN | NaN | 0.010 | 0.0 | 0.505 | 0.0 | See | See |
| 21 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | NaN | 0.006 | 0.0 | 0.260 | 0.0 | -1 | 1 | NaN | NaN | 0.010 | 0.0 | 0.607 | 0.0 | See | See |
| 24 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | NaN | 0.005 | 0.0 | 0.300 | 0.0 | 1 | 100 | NaN | NaN | 0.010 | 0.0 | 0.525 | 0.0 | See | See |
| 27 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | NaN | 0.005 | 0.0 | 0.302 | 0.0 | 1 | 1 | NaN | NaN | 0.010 | 0.0 | 0.513 | 0.0 | See | See |
| 30 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | NaN | 0.005 | 0.0 | 0.300 | 0.0 | -1 | 5 | NaN | NaN | 0.010 | 0.0 | 0.523 | 0.0 | See | See |
| 33 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | NaN | 0.005 | 0.0 | 0.301 | 0.0 | 1 | 5 | NaN | NaN | 0.010 | 0.0 | 0.529 | 0.0 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_sklearnex | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | NaN | 2.778 | 0.052 | 0.000 | 0.003 | -1 | 100 | 0.853 | 0.846 | 0.243 | 0.003 | 11.452 | 0.272 | See | See |
| 2 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | NaN | 0.026 | 0.003 | 0.000 | 0.026 | -1 | 100 | 1.000 | 1.000 | 0.008 | 0.000 | 3.088 | 0.378 | See | See |
| 4 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | NaN | 1.915 | 0.029 | 0.000 | 0.002 | -1 | 1 | 0.669 | 0.634 | 0.196 | 0.002 | 9.785 | 0.184 | See | See |
| 5 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | NaN | 0.024 | 0.003 | 0.000 | 0.024 | -1 | 1 | 1.000 | 1.000 | 0.008 | 0.000 | 2.931 | 0.336 | See | See |
| 7 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | NaN | 2.053 | 0.021 | 0.000 | 0.002 | 1 | 100 | 0.853 | 0.634 | 0.195 | 0.005 | 10.521 | 0.268 | See | See |
| 8 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | NaN | 0.021 | 0.000 | 0.000 | 0.021 | 1 | 100 | 1.000 | 1.000 | 0.008 | 0.000 | 2.551 | 0.124 | See | See |
| 10 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | NaN | 1.222 | 0.006 | 0.001 | 0.001 | 1 | 1 | 0.669 | 0.725 | 0.195 | 0.004 | 6.257 | 0.123 | See | See |
| 11 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | NaN | 0.021 | 0.001 | 0.000 | 0.021 | 1 | 1 | 1.000 | 1.000 | 0.009 | 0.001 | 2.402 | 0.156 | See | See |
| 13 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | NaN | 2.815 | 0.032 | 0.000 | 0.003 | -1 | 5 | 0.755 | 0.846 | 0.238 | 0.005 | 11.806 | 0.293 | See | See |
| 14 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | NaN | 0.025 | 0.003 | 0.000 | 0.025 | -1 | 5 | 1.000 | 1.000 | 0.008 | 0.000 | 3.012 | 0.360 | See | See |
| 16 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | NaN | 2.057 | 0.010 | 0.000 | 0.002 | 1 | 5 | 0.755 | 0.725 | 0.198 | 0.002 | 10.365 | 0.109 | See | See |
| 17 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | NaN | 0.020 | 0.000 | 0.000 | 0.020 | 1 | 5 | 1.000 | 1.000 | 0.008 | 0.000 | 2.406 | 0.087 | See | See |
| 19 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | NaN | 2.630 | 0.030 | 0.000 | 0.003 | -1 | 100 | 0.867 | 0.872 | 0.072 | 0.001 | 36.691 | 0.637 | See | See |
| 20 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | NaN | 0.011 | 0.002 | 0.000 | 0.011 | -1 | 100 | 1.000 | 1.000 | 0.001 | 0.000 | 11.580 | 2.470 | See | See |
| 22 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | NaN | 1.765 | 0.029 | 0.000 | 0.002 | -1 | 1 | 0.835 | 0.825 | 0.031 | 0.001 | 56.898 | 1.344 | See | See |
| 23 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | NaN | 0.009 | 0.002 | 0.000 | 0.009 | -1 | 1 | 1.000 | 0.000 | 0.001 | 0.000 | 10.449 | 2.810 | See | See |
| 25 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | NaN | 2.176 | 0.133 | 0.000 | 0.002 | 1 | 100 | 0.867 | 0.825 | 0.031 | 0.001 | 70.246 | 4.456 | See | See |
| 26 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | NaN | 0.006 | 0.001 | 0.000 | 0.006 | 1 | 100 | 1.000 | 0.000 | 0.001 | 0.000 | 7.881 | 2.370 | See | See |
| 28 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | NaN | 1.234 | 0.030 | 0.000 | 0.001 | 1 | 1 | 0.835 | 0.863 | 0.033 | 0.000 | 37.244 | 0.932 | See | See |
| 29 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | NaN | 0.003 | 0.002 | 0.000 | 0.003 | 1 | 1 | 1.000 | 1.000 | 0.001 | 0.000 | 3.723 | 2.113 | See | See |
| 31 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | NaN | 2.861 | 0.145 | 0.000 | 0.003 | -1 | 5 | 0.862 | 0.872 | 0.074 | 0.001 | 38.901 | 2.075 | See | See |
| 32 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | NaN | 0.010 | 0.002 | 0.000 | 0.010 | -1 | 5 | 1.000 | 1.000 | 0.001 | 0.000 | 9.851 | 2.549 | See | See |
| 34 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | NaN | 1.943 | 0.017 | 0.000 | 0.002 | 1 | 5 | 0.862 | 0.863 | 0.032 | 0.000 | 60.416 | 0.694 | See | See |
| 35 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | 1 | 5 | 1.000 | 1.000 | 0.001 | 0.000 | 3.894 | 0.778 | See | See |
KMeans_tall: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_sklearnex | adjusted_rand_score_sklearnex | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.669 | 0.0 | 0.717 | 0.0 | random | NaN | 30 | NaN | 0.431 | 0.0 | 1.553 | 0.0 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.634 | 0.0 | 0.758 | 0.0 | k-means++ | NaN | 30 | NaN | 0.507 | 0.0 | 1.250 | 0.0 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 7.185 | 0.0 | 3.340 | 0.0 | random | NaN | 30 | NaN | 2.831 | 0.0 | 2.538 | 0.0 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.610 | 0.0 | 3.631 | 0.0 | k-means++ | NaN | 30 | NaN | 2.994 | 0.0 | 2.208 | 0.0 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_sklearnex | adjusted_rand_score_sklearnex | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.002 | 0.000 | 0.297 | 0.000 | random | 0.001 | 30 | 0.001 | 0.0 | 0.0 | 9.895 | 6.143 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.002 | 0.000 | 0.000 | 0.002 | random | 1.000 | 30 | 1.000 | 0.0 | 0.0 | 11.687 | 7.930 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.002 | 0.000 | 0.297 | 0.000 | k-means++ | 0.001 | 30 | 0.001 | 0.0 | 0.0 | 10.087 | 6.708 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.002 | 0.000 | 0.000 | 0.002 | k-means++ | 1.000 | 30 | 1.000 | 0.0 | 0.0 | 12.083 | 7.556 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.000 | 12.977 | 0.000 | random | 0.002 | 30 | 0.002 | 0.0 | 0.0 | 6.311 | 2.426 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.002 | 0.001 | 0.011 | 0.002 | random | 1.000 | 30 | 1.000 | 0.0 | 0.0 | 14.662 | 10.850 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.000 | 12.917 | 0.000 | k-means++ | 0.001 | 30 | 0.002 | 0.0 | 0.0 | 6.558 | 2.578 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.002 | 0.000 | 0.016 | 0.002 | k-means++ | 1.000 | 30 | 1.000 | 0.0 | 0.0 | 9.156 | 4.606 | See | See |
KMeans_short: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_sklearnex | adjusted_rand_score_sklearnex | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.086 | 0.0 | 0.037 | 0.0 | random | NaN | 20 | NaN | 0.032 | 0.0 | 2.688 | 0.0 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.249 | 0.0 | 0.013 | 0.0 | k-means++ | NaN | 20 | NaN | 0.107 | 0.0 | 2.333 | 0.0 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.234 | 0.0 | 0.685 | 0.0 | random | NaN | 20 | NaN | 0.125 | 0.0 | 1.869 | 0.0 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.677 | 0.0 | 0.236 | 0.0 | k-means++ | NaN | 20 | NaN | 0.349 | 0.0 | 1.944 | 0.0 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_sklearnex | adjusted_rand_score_sklearnex | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.156 | 0.000 | random | 0.003 | 20 | -0.000 | 0.001 | 0.0 | 3.617 | 0.891 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.002 | 0.0 | 0.000 | 0.002 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 10.416 | 5.887 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.150 | 0.000 | k-means++ | 0.005 | 20 | 0.001 | 0.001 | 0.0 | 3.753 | 0.757 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.002 | 0.0 | 0.000 | 0.002 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 11.092 | 6.901 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.0 | 5.467 | 0.000 | random | 0.387 | 20 | 0.305 | 0.001 | 0.0 | 2.436 | 0.527 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.002 | 0.0 | 0.010 | 0.002 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.341 | 5.071 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.0 | 5.375 | 0.000 | k-means++ | 0.283 | 20 | 0.317 | 0.001 | 0.0 | 2.586 | 0.432 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.002 | 0.0 | 0.010 | 0.002 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.027 | 4.807 | See | See |
LogisticRegression: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)¶All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | [20] | 12.044 | 0.0 | [-0.09796309] | 0.000 | NaN | NaN | NaN | NaN | NaN | 1.977 | 0.0 | 6.091 | 0.0 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | [27] | 0.877 | 0.0 | [-2.43572838] | 0.001 | NaN | NaN | NaN | NaN | NaN | 0.749 | 0.0 | 1.170 | 0.0 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | [20] | 0.000 | 0.0 | [51.38246118] | 0.0 | NaN | NaN | NaN | NaN | 0.519 | 0.000 | 0.0 | 0.835 | 0.378 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | [20] | 0.000 | 0.0 | [0.2220298] | 0.0 | NaN | NaN | NaN | NaN | 0.000 | 0.000 | 0.0 | 0.394 | 0.350 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | [27] | 0.002 | 0.0 | [125.0769281] | 0.0 | NaN | NaN | NaN | NaN | 0.290 | 0.003 | 0.0 | 0.528 | 0.098 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | [27] | 0.000 | 0.0 | [24.19815601] | 0.0 | NaN | NaN | NaN | NaN | 0.000 | 0.001 | 0.0 | 0.118 | 0.090 | See | See |
Ridge: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)¶All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | NaN | 0.184 | 0.0 | 0.434 | 0.0 | NaN | NaN | NaN | 0.192 | 0.0 | 0.960 | 0.0 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | NaN | 1.437 | 0.0 | 0.557 | 0.0 | NaN | NaN | NaN | 0.254 | 0.0 | 5.657 | 0.0 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Ridge | predict | 1000 | 1000 | 10000 | NaN | 0.01 | 0.0 | 7.959 | 0.0 | NaN | NaN | 0.116 | 0.017 | 0.0 | 0.593 | 0.020 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | NaN | 0.00 | 0.0 | 1.051 | 0.0 | NaN | NaN | NaN | 0.000 | 0.0 | 0.594 | 0.562 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | NaN | 0.00 | 0.0 | 5.426 | 0.0 | NaN | NaN | 1.000 | 0.000 | 0.0 | 0.577 | 0.376 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | NaN | 0.00 | 0.0 | 0.012 | 0.0 | NaN | NaN | NaN | 0.000 | 0.0 | 0.653 | 0.643 | See | See |